333 lines
11 KiB
Python
333 lines
11 KiB
Python
# utils/csv_processor.py
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"""CSV processor utilities ....
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This module provides classes and functions for:
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- ....
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- ....
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- ....
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"""
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import logging
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from datetime import datetime
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from io import StringIO
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from typing import Any, Dict
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import pandas as pd
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import requests
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from sqlalchemy import literal
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from sqlalchemy.orm import Session
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from models.database_models import Product
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logger = logging.getLogger(__name__)
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class CSVProcessor:
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"""Handles CSV import with robust parsing and batching."""
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ENCODINGS = ["utf-8", "latin-1", "iso-8859-1", "cp1252", "utf-8-sig"]
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PARSING_CONFIGS = [
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# Try auto-detection first
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{"sep": None, "engine": "python"},
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# Try semicolon (common in European CSVs)
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{"sep": ";", "engine": "python"},
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# Try comma
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{"sep": ",", "engine": "python"},
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# Try tab
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{"sep": "\t", "engine": "python"},
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]
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COLUMN_MAPPING = {
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# Standard variations
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"id": "product_id",
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"ID": "product_id",
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"Product ID": "product_id",
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"name": "title",
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"Name": "title",
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"product_name": "title",
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"Product Name": "title",
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# Google Shopping feed standard
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"g:id": "product_id",
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"g:title": "title",
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"g:description": "description",
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"g:link": "link",
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"g:image_link": "image_link",
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"g:availability": "availability",
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"g:price": "price",
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"g:brand": "brand",
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"g:gtin": "gtin",
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"g:mpn": "mpn",
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"g:condition": "condition",
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"g:adult": "adult",
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"g:multipack": "multipack",
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"g:is_bundle": "is_bundle",
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"g:age_group": "age_group",
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"g:color": "color",
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"g:gender": "gender",
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"g:material": "material",
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"g:pattern": "pattern",
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"g:size": "size",
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"g:size_type": "size_type",
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"g:size_system": "size_system",
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"g:item_group_id": "item_group_id",
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"g:google_product_category": "google_product_category",
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"g:product_type": "product_type",
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"g:custom_label_0": "custom_label_0",
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"g:custom_label_1": "custom_label_1",
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"g:custom_label_2": "custom_label_2",
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"g:custom_label_3": "custom_label_3",
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"g:custom_label_4": "custom_label_4",
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# Handle complex shipping column
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"shipping(country:price:max_handling_time:min_transit_time:max_transit_time)": "shipping",
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}
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def __init__(self):
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"""Class constructor."""
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from utils.data_processing import GTINProcessor, PriceProcessor
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self.gtin_processor = GTINProcessor()
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self.price_processor = PriceProcessor()
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def download_csv(self, url: str) -> str:
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"""Download and decode CSV with multiple encoding attempts."""
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try:
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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content = response.content
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# Try different encodings
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for encoding in self.ENCODINGS:
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try:
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decoded_content = content.decode(encoding)
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logger.info(f"Successfully decoded CSV with encoding: {encoding}")
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return decoded_content
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except UnicodeDecodeError:
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continue
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# Fallback with error ignoring
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decoded_content = content.decode("utf-8", errors="ignore")
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logger.warning("Used UTF-8 with error ignoring for CSV decoding")
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return decoded_content
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except requests.RequestException as e:
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logger.error(f"Error downloading CSV: {e}")
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raise
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def parse_csv(self, csv_content: str) -> pd.DataFrame:
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"""Parse CSV with multiple separator attempts."""
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for config in self.PARSING_CONFIGS:
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try:
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df = pd.read_csv(
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StringIO(csv_content),
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on_bad_lines="skip",
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quotechar='"',
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skip_blank_lines=True,
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skipinitialspace=True,
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**config,
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)
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logger.info(f"Successfully parsed CSV with config: {config}")
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return df
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except pd.errors.ParserError:
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continue
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raise pd.errors.ParserError("Could not parse CSV with any configuration")
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def normalize_columns(self, df: pd.DataFrame) -> pd.DataFrame:
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"""Normalize column names using mapping."""
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# Clean column names
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df.columns = df.columns.str.strip()
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# Apply mapping
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df = df.rename(columns=self.COLUMN_MAPPING)
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logger.info(f"Normalized columns: {list(df.columns)}")
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return df
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def _clean_row_data(self, row_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Process a single row with data normalization."""
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# Handle NaN values
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processed_data = {k: (v if pd.notna(v) else None) for k, v in row_data.items()}
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# Process GTIN
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if processed_data.get("gtin"):
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processed_data["gtin"] = self.gtin_processor.normalize(
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processed_data["gtin"]
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)
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# Process price and currency
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if processed_data.get("price"):
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parsed_price, currency = self.price_processor.parse_price_currency(
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processed_data["price"]
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)
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processed_data["price"] = parsed_price
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processed_data["currency"] = currency
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# Process sale_price
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if processed_data.get("sale_price"):
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parsed_sale_price, _ = self.price_processor.parse_price_currency(
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processed_data["sale_price"]
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)
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processed_data["sale_price"] = parsed_sale_price
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# Clean MPN (remove .0 endings)
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if processed_data.get("mpn"):
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mpn_str = str(processed_data["mpn"]).strip()
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if mpn_str.endswith(".0"):
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processed_data["mpn"] = mpn_str[:-2]
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# Handle multipack type conversion
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if processed_data.get("multipack") is not None:
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try:
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processed_data["multipack"] = int(float(processed_data["multipack"]))
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except (ValueError, TypeError):
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processed_data["multipack"] = None
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return processed_data
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async def process_marketplace_csv_from_url(
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self, url: str, marketplace: str, shop_name: str, batch_size: int, db: Session
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) -> Dict[str, Any]:
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"""
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Process CSV from URL with marketplace and shop information.
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Args:
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url: URL to the CSV file
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marketplace: Name of the marketplace (e.g., 'Letzshop', 'Amazon')
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shop_name: Name of the shop
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batch_size: Number of rows to process in each batch
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db: Database session
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Returns:
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Dictionary with processing results
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"""
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logger.info(
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f"Starting marketplace CSV import from {url} for {marketplace} -> {shop_name}"
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)
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# Download and parse CSV
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csv_content = self.download_csv(url)
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df = self.parse_csv(csv_content)
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df = self.normalize_columns(df)
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logger.info(f"Processing CSV with {len(df)} rows and {len(df.columns)} columns")
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imported = 0
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updated = 0
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errors = 0
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# Process in batches
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for i in range(0, len(df), batch_size):
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batch_df = df.iloc[i : i + batch_size]
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batch_result = await self._process_marketplace_batch(
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batch_df, marketplace, shop_name, db, i // batch_size + 1
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)
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imported += batch_result["imported"]
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updated += batch_result["updated"]
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errors += batch_result["errors"]
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logger.info(f"Processed batch {i // batch_size + 1}: {batch_result}")
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return {
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"total_processed": imported + updated + errors,
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"imported": imported,
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"updated": updated,
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"errors": errors,
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"marketplace": marketplace,
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"shop_name": shop_name,
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}
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async def _process_marketplace_batch(
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self,
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batch_df: pd.DataFrame,
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marketplace: str,
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shop_name: str,
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db: Session,
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batch_num: int,
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) -> Dict[str, int]:
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"""Process a batch of CSV rows with marketplace information."""
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imported = 0
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updated = 0
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errors = 0
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logger.info(
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f"Processing batch {batch_num} with {len(batch_df)} rows for "
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f"{marketplace} -> {shop_name}"
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)
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for index, row in batch_df.iterrows():
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try:
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# Convert row to dictionary and clean up
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product_data = self._clean_row_data(row.to_dict())
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# Add marketplace and shop information
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product_data["marketplace"] = marketplace
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product_data["shop_name"] = shop_name
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# Validate required fields
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if not product_data.get("product_id"):
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logger.warning(f"Row {index}: Missing product_id, skipping")
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errors += 1
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continue
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if not product_data.get("title"):
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logger.warning(f"Row {index}: Missing title, skipping")
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errors += 1
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continue
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# Check if product exists
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existing_product = (
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db.query(Product)
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.filter(Product.product_id == literal(product_data["product_id"]))
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.first()
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)
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if existing_product:
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# Update existing product
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for key, value in product_data.items():
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if key not in ["id", "created_at"] and hasattr(
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existing_product, key
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):
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setattr(existing_product, key, value)
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existing_product.updated_at = datetime.utcnow()
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updated += 1
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logger.debug(
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f"Updated product {product_data['product_id']} for "
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f"{marketplace} and shop {shop_name}"
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)
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else:
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# Create new product
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filtered_data = {
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k: v
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for k, v in product_data.items()
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if k not in ["id", "created_at", "updated_at"]
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and hasattr(Product, k)
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}
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new_product = Product(**filtered_data)
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db.add(new_product)
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imported += 1
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logger.debug(
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f"Imported new product {product_data['product_id']} "
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f"for {marketplace} and shop {shop_name}"
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)
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except Exception as e:
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logger.error(f"Error processing row: {e}")
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errors += 1
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continue
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# Commit the batch
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try:
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db.commit()
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logger.info(f"Batch {batch_num} committed successfully")
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except Exception as e:
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logger.error(f"Failed to commit batch {batch_num}: {e}")
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db.rollback()
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# Count all rows in this batch as errors
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errors = len(batch_df)
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imported = 0
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updated = 0
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return {"imported": imported, "updated": updated, "errors": errors}
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